Storage Cluster Lifecycle Management for Time-Data Segmentation
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Solution Overview
Problem
Large-scale computing systems face challenges in efficiently managing and querying large volumes of time-segmented data, leading to performance issues and increased complexity due to the need for continuous data storage and retrieval across multiple interacting services.
Innovation Solution
Implementing a lifecycle management system for storage clusters that segments data across multiple clusters based on timestamps, allowing for clusters to be launched, initialized, made read-only, optimized, and terminated according to data retention policies, with snapshots maintained for potential restoration, enabling efficient data storage and querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a single large cluster, then storage capacity is sufficient, but management complexity and query performance deteriorate
Solution Approach 1:
The patent divides the storage system into multiple time-segmented clusters, where each cluster stores data for a specific time period. This segmentation allows independent management of each cluster through its lifecycle stages, reducing overall system complexity while maintaining sufficient total storage capacity across all clusters
2Speed
If data is continuously stored in active clusters, then data retrieval is fast, but system performance and resource utilization deteriorate
Solution Approach 1:
The patent implements dynamic lifecycle management where clusters transition between different states (loading, read-write, read-only, termination) based on their age and data access patterns. This dynamic state management optimizes system performance by allowing older clusters to be taken offline for optimization while maintaining fast access to active clusters
Solution Approach 2:
The system periodically transitions clusters through defined lifecycle stages, with automatic promotion to read-only status and scheduled optimization operations. This periodic action ensures consistent performance maintenance without continuous manual intervention
3Adaptability or versatility
If manual cluster management is performed, then flexibility is high, but administrative overhead and time consumption increase
Solution Approach 1:
The patent implements automated lifecycle management where the system itself manages cluster creation, data loading, state transitions, optimization, and termination based on predefined policies. This self-service approach maintains flexibility through configurable policies while eliminating manual administrative time consumption
4Speed
If data is segmented across multiple clusters, then query performance improves, but data repartitioning complexity increases
Solution Approach 1:
The patent performs data segmentation in advance by assigning data to clusters based on time periods before the clusters are created. This preliminary action eliminates the need for complex runtime repartitioning operations, as data is already optimally distributed when clusters are provisioned
Data Source
AI summary
Methods, systems, and computer-readable media for lifecycle management of multiple storage clusters are disclosed. A first set of data is loaded into a first storage cluster. The first storage cluster is selected for loading the first set of data based at least in part on the first set of data and the first storage cluster being associated with a first period of time. The first storage cluster is placed into a read-only state. A second set of data is loaded into a second storage cluster. The second storage cluster is selected for loading the second set of data based at least in part on the second set of data and the second storage cluster being associated with a second period of time.